دورية أكاديمية
Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia
| العنوان: | Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia |
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| المؤلفون: | Gaubert, Malo, Dell’Orco, Andrea, Lange, Catharina, Garnier-Crussard, Antoine, Zimmermann, Isabella, Dyrba, Martin, Duering, Marco, Ziegler, Gabriel, Peters, Oliver, Preis, Lukas, Priller, Josef, Spruth, Eike Jakob, Schneider, Anja, Fliessbach, Klaus, Wiltfang, Jens, Schott, Björn H., Maier, Franziska, Glanz, Wenzel, Buerger, Katharina, Janowitz, Daniel, Perneczky, Robert, Rauchmann, Boris-Stephan, Teipel, Stefan, Kilimann, Ingo, Laske, Christoph, Munk, Matthias H., Spottke, Annika, Roy, Nina, Dobisch, Laura, Ewers, Michael, Dechent, Peter, Haynes, John Dylan, Scheffler, Klaus, Düzel, Emrah, Jessen, Frank, Wirth, Miranka |
| المساهمون: | Gaubert, Malo, 1German Center for Neurodegenerative Diseases, Dresden, Germany, Dell’Orco, Andrea, Lange, Catharina, Garnier-Crussard, Antoine, 5Clinical and Research Memory Center of Lyon, Lyon Institute for Elderly, Hospices Civils de Lyon, Lyon, France, Zimmermann, Isabella, Dyrba, Martin, 8German Center for Neurodegenerative Diseases, Rostock, Germany, Duering, Marco, 9Department of Biomedical Engineering, Medical Image Analysis Center (MIAC) and qbig, University of Basel, Basel, Switzerland, Ziegler, Gabriel, 10German Center for Neurodegenerative Diseases, Magdeburg, Germany, Peters, Oliver, 11German Center for Neurodegenerative Diseases, Berlin, Germany, Preis, Lukas, 12Department of Psychiatry, Charité – Universitätsmedizin Berlin, Berlin, Germany, Priller, Josef, Spruth, Eike Jakob, Schneider, Anja, 16German Center for Neurodegenerative Diseases, Bonn, Germany, Fliessbach, Klaus, Wiltfang, Jens, 18German Center for Neurodegenerative Diseases, Göttingen, Germany, Schott, Björn H., Maier, Franziska, 22Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany, Glanz, Wenzel, Buerger, Katharina, 23German Center for Neurodegenerative Diseases, Munich, Germany, Janowitz, Daniel, 24Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University of Munich (LMU), Munich, Germany, Perneczky, Robert, Rauchmann, Boris-Stephan, Teipel, Stefan, Kilimann, Ingo, Laske, Christoph, 29German Center for Neurodegenerative Diseases, Tübingen, Germany, Munk, Matthias H., Spottke, Annika, Roy, Nina, Dobisch, Laura, Ewers, Michael, Dechent, Peter, 32MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University of Göttingen, Göttingen, Germany, Haynes, John Dylan, 33Bernstein Center for Computational Neuroscience, Charité – Universitätsmedizin, Berlin, Germany, Scheffler, Klaus, 34Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany, Düzel, Emrah, Jessen, Frank, Wirth, Miranka |
| سنة النشر: | 2023 |
| المجموعة: | Georg-August-Universität Göttingen: GoeScholar |
| الوصف: | Background White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research. Methods We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS). Results Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions. Conclusion To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1664-0640 |
| العلاقة: | https://resolver.sub.uni-goettingen.de/purl?gro-2/119795Test |
| DOI: | 10.3389/fpsyt.2022.1010273 |
| الإتاحة: | https://doi.org/10.3389/fpsyt.2022.1010273Test https://resolver.sub.uni-goettingen.de/purl?gro-2/119795Test |
| حقوق: | CC BY 4.0 ; http://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: | edsbas.B05DE9FC |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 16640640 |
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| DOI: | 10.3389/fpsyt.2022.1010273 |